Menze et al. [1]} proposed the BraTS dataset together with the organizers of the Medical Image Computing & Computer-Assisted Intervention (MICCAI) 2012 and 2013 conferences, as a benchmark for the conference's tumor segmentation challenge, which takes place annually with eventual changes in BraTS data. The 2015 and 2019 versions of this dataset [2]}, [3]}, [4]}, [5]}, [6]} were used for this work. Both datasets are publicly available and are originally split into training and validation sets with several study cases with one volume of each type among FLAIR, T1, T1c and T2. The training sets also contain one extra volume per case which is the ground truth segmentation of the tumor. A total of 1,840 volumes in NIfTI format equally distributed among the four MRI sequence types compose the BraTS 2019 dataset (1,340 from the training set and 500 from the validation set), while the 2015 version contains a total of 1,536 also balanced volumes in MetaImage format (1,096 from the training set and 440 from the validation set). The BraTS datasets provide pre-processed data, i.e., data previously subject to several processing steps. The most visible one is the skull-stripping, i.e., the removal of the crane's skull from the images. An example of BraTS pre-processed data is shown in Figure REF .
